Tags

• RNA-seq 2
• exprAnalysis 2
• Illumina 1
• Affymetrix 1
• DESeq2 2
• ggplot2 32
• networks 2
• cummeRbund 1
• PCA 2
• heatmap 2
• WGCNA 1
• maps 4
• gpx 2
• ggmap 4
• XML 1
• Google_maps 1
• API 1
• AnnotationDbi 2
• genes 2
• genomics 2
• genetics 2
• SYSK 1
• sentiment_analysis 1
• text_mining 3
• Gilmore_Girls 2
• network 1
• cooccurrence 1
• igraph 5
• Machine_Learning 3
• random_forest 6
• Random_Forest 2
• blogging 2
• github 2
• jekyll 1
• bootstrap 1
• AnnotatioDbi 3
• dendextend 1
• networkD3 1
• biomaRt 2
• shiny 5
• gwas 1
• ttbbeer 1
• gganimate 1
• animation 1
• google 1
• RFE 1
• GA 1
• R 1
• Python 1
• dplyr 1
• magrittr 1
• pandas 1
• matplotlib 1
• gender 1
• statistics 1
• qtl 1
• functions 1
• machine_learning 13
• spark 3
• h2o 3
• neural_nets 2
• deep_learning 2
• grid_search 1
• caret 3
• ggraph 1
• glm 1
• plot3d 1
• 3D 1
• gis 1
• lime 1
• neural_network 2
• oneR 1
• autoencoder 3
• anomaly 3
• fraud 3
• sna 1
• r_users_group 1
• timeseries 3
• forecasting 3
• data_preparation 1
• ggplot 6
• tidyverse 6
• timekit 2
• text_analysis 3
• tidytext 2
• tesseract 1
• ocr 1
• gitlab 1
• blogdown 2
• hugo 1
• netlify 1
• bookdown 1
• webinar 1
• data_science 2
• artificial_intelligence 1
• business_intelligence 1
• scalability 1
• flexdashboard 1
• predictive_maintenance 1

RNA-seq

• DESeq2 Course Work
• exprAnalysis package

exprAnalysis

• DESeq2 Course Work
• exprAnalysis package

Illumina

• exprAnalysis package

Affymetrix

• exprAnalysis package

DESeq2

• DESeq2 Course Work
• exprAnalysis package

ggplot2

• Data Science for Fraud Detection
• Update to autoencoders and anomaly detection with machine learning in fraud analytics
• Autoencoders and anomaly detection with machine learning in fraud analytics
• Does money buy happiness after all? Machine Learning with One Rule
• Explaining complex machine learning models with LIME
• Happy EasteR: Plotting hare populations in Germany
• Data on tour: Plotting 3D maps and location tracks
• Dealing with unbalanced data in machine learning
• Building meaningful machine learning models for disease prediction
• Plotting trees from Random Forest models with ggraph
• Hyper-parameter Tuning with Grid Search for Deep Learning
• Building deep neural nets with h2o and rsparkling that predict arrhythmia of the heart
• Predicting food preferences with sparklyr (machine learning)
• Conditional ggplot2 geoms in functions (QTL plots)
• Scratching the Surface of Gender Biases
• New features in World Gender Statistics app
• Exploring World Gender Statistics with Shiny
• R vs Python - a One-on-One Comparison
• Feature Selection in Machine Learning (Breast Cancer Datasets)
• Gene homology Part 3 - Visualizing Gene Ontology of Conserved Genes
• How to map your Google location history with R
• Animating Plots of Beer Ingredients and Sin Taxes over Time
• How to build a Shiny app for disease- & trait-associated locations of the human genome
• Creating a network of human gene homology with R and D3
• Extreme Gradient Boosting and Preprocessing in Machine Learning - Addendum to predicting flu outcome with R
• Can we predict flu deaths with Machine Learning and R?
• Analysing the Gilmore Girls' coffee addiction with R
• Exploring the human genome (Part 2) - Transcripts
• Exploring the human genome (Part 1) - Gene Annotations
• USA/ Canada Roadtrip 2016
• DESeq2 Course Work
• exprAnalysis package

networks

• Network analysis of Game of Thrones family ties
• exprAnalysis package

cummeRbund

• exprAnalysis package

PCA

• DESeq2 Course Work
• exprAnalysis package

heatmap

• DESeq2 Course Work
• exprAnalysis package

WGCNA

• exprAnalysis package

maps

• Happy EasteR: Plotting hare populations in Germany
• Data on tour: Plotting 3D maps and location tracks
• How to map your Google location history with R
• USA/ Canada Roadtrip 2016

gpx

• Data on tour: Plotting 3D maps and location tracks
• USA/ Canada Roadtrip 2016

ggmap

• New R Users group in Münster!
• Data on tour: Plotting 3D maps and location tracks
• How to map your Google location history with R
• USA/ Canada Roadtrip 2016

XML

• USA/ Canada Roadtrip 2016

Google_maps

• USA/ Canada Roadtrip 2016

API

• USA/ Canada Roadtrip 2016

AnnotationDbi

• Exploring the human genome (Part 2) - Transcripts
• Exploring the human genome (Part 1) - Gene Annotations

genes

• Exploring the human genome (Part 2) - Transcripts
• Exploring the human genome (Part 1) - Gene Annotations

genomics

• Exploring the human genome (Part 2) - Transcripts
• Exploring the human genome (Part 1) - Gene Annotations

genetics

• Exploring the human genome (Part 2) - Transcripts
• Exploring the human genome (Part 1) - Gene Annotations

SYSK

• Is 'Yeah' Josh and Chuck's favorite word?

sentiment_analysis

• Is 'Yeah' Josh and Chuck's favorite word?

text_mining

• Analysing the Gilmore Girls' coffee addiction with R
• Creating a Gilmore Girls character network with R
• Is 'Yeah' Josh and Chuck's favorite word?

Gilmore_Girls

• Analysing the Gilmore Girls' coffee addiction with R
• Creating a Gilmore Girls character network with R

network

• Creating a Gilmore Girls character network with R

cooccurrence

• Creating a Gilmore Girls character network with R

igraph

• Network analysis of Game of Thrones family ties
• Plotting trees from Random Forest models with ggraph
• Gene homology Part 3 - Visualizing Gene Ontology of Conserved Genes
• Gene homology Part 2 - creating directed networks with igraph
• Creating a Gilmore Girls character network with R

Machine_Learning

• Feature Selection in Machine Learning (Breast Cancer Datasets)
• Extreme Gradient Boosting and Preprocessing in Machine Learning - Addendum to predicting flu outcome with R
• Can we predict flu deaths with Machine Learning and R?

random_forest

• Does money buy happiness after all? Machine Learning with One Rule
• Dealing with unbalanced data in machine learning
• Building meaningful machine learning models for disease prediction
• Plotting trees from Random Forest models with ggraph
• Predicting food preferences with sparklyr (machine learning)
• Can we predict flu deaths with Machine Learning and R?

Random_Forest

• Feature Selection in Machine Learning (Breast Cancer Datasets)
• Extreme Gradient Boosting and Preprocessing in Machine Learning - Addendum to predicting flu outcome with R

blogging

• From Biology to Industry. A Blogger’s Journey to Data Science.
• How to set up your own R blog with Github pages and Jekyll Bootstrap

github

• Migrating from GitHub to GitLab with RStudio (Tutorial)
• How to set up your own R blog with Github pages and Jekyll Bootstrap

jekyll

• How to set up your own R blog with Github pages and Jekyll Bootstrap

bootstrap

• How to set up your own R blog with Github pages and Jekyll Bootstrap

AnnotatioDbi

• Gene homology Part 3 - Visualizing Gene Ontology of Conserved Genes
• Gene homology Part 2 - creating directed networks with igraph
• Creating a network of human gene homology with R and D3

dendextend

• Creating a network of human gene homology with R and D3

networkD3

• Creating a network of human gene homology with R and D3

biomaRt

• Gene homology Part 3 - Visualizing Gene Ontology of Conserved Genes
• Gene homology Part 2 - creating directed networks with igraph

shiny

• Explore Predictive Maintenance with flexdashboard
• Scratching the Surface of Gender Biases
• New features in World Gender Statistics app
• Exploring World Gender Statistics with Shiny
• How to build a Shiny app for disease- & trait-associated locations of the human genome

gwas

• How to build a Shiny app for disease- & trait-associated locations of the human genome

ttbbeer

• Animating Plots of Beer Ingredients and Sin Taxes over Time

gganimate

• Animating Plots of Beer Ingredients and Sin Taxes over Time

animation

• Animating Plots of Beer Ingredients and Sin Taxes over Time

google

• How to map your Google location history with R

RFE

• Feature Selection in Machine Learning (Breast Cancer Datasets)

GA

• Feature Selection in Machine Learning (Breast Cancer Datasets)

R

• R vs Python - a One-on-One Comparison

Python

• R vs Python - a One-on-One Comparison

dplyr

• R vs Python - a One-on-One Comparison

magrittr

• R vs Python - a One-on-One Comparison

pandas

• R vs Python - a One-on-One Comparison

matplotlib

• R vs Python - a One-on-One Comparison

gender

• Scratching the Surface of Gender Biases

statistics

• Scratching the Surface of Gender Biases

qtl

• Conditional ggplot2 geoms in functions (QTL plots)

functions

• Conditional ggplot2 geoms in functions (QTL plots)

machine_learning

• Blockchain & distributed ML - my report from the data2day conference
• From Biology to Industry. A Blogger’s Journey to Data Science.
• Data Science for Fraud Detection
• Update to autoencoders and anomaly detection with machine learning in fraud analytics
• Autoencoders and anomaly detection with machine learning in fraud analytics
• Does money buy happiness after all? Machine Learning with One Rule
• Explaining complex machine learning models with LIME
• Dealing with unbalanced data in machine learning
• Building meaningful machine learning models for disease prediction
• Plotting trees from Random Forest models with ggraph
• Hyper-parameter Tuning with Grid Search for Deep Learning
• Building deep neural nets with h2o and rsparkling that predict arrhythmia of the heart
• Predicting food preferences with sparklyr (machine learning)

spark

• Hyper-parameter Tuning with Grid Search for Deep Learning
• Building deep neural nets with h2o and rsparkling that predict arrhythmia of the heart
• Predicting food preferences with sparklyr (machine learning)

h2o

• Building meaningful machine learning models for disease prediction
• Hyper-parameter Tuning with Grid Search for Deep Learning
• Building deep neural nets with h2o and rsparkling that predict arrhythmia of the heart

neural_nets

• Hyper-parameter Tuning with Grid Search for Deep Learning
• Building deep neural nets with h2o and rsparkling that predict arrhythmia of the heart

deep_learning

• Hyper-parameter Tuning with Grid Search for Deep Learning
• Building deep neural nets with h2o and rsparkling that predict arrhythmia of the heart

grid_search

• Hyper-parameter Tuning with Grid Search for Deep Learning

caret

• Dealing with unbalanced data in machine learning
• Building meaningful machine learning models for disease prediction
• Plotting trees from Random Forest models with ggraph

ggraph

• Plotting trees from Random Forest models with ggraph

glm

• Building meaningful machine learning models for disease prediction

plot3d

• Data on tour: Plotting 3D maps and location tracks

3D

• Data on tour: Plotting 3D maps and location tracks

gis

• Data on tour: Plotting 3D maps and location tracks

lime

• Explaining complex machine learning models with LIME

neural_network

• Does money buy happiness after all? Machine Learning with One Rule
• Explaining complex machine learning models with LIME

oneR

• Does money buy happiness after all? Machine Learning with One Rule

autoencoder

• Data Science for Fraud Detection
• Update to autoencoders and anomaly detection with machine learning in fraud analytics
• Autoencoders and anomaly detection with machine learning in fraud analytics

anomaly

• Data Science for Fraud Detection
• Update to autoencoders and anomaly detection with machine learning in fraud analytics
• Autoencoders and anomaly detection with machine learning in fraud analytics

fraud

• Data Science for Fraud Detection
• Update to autoencoders and anomaly detection with machine learning in fraud analytics
• Autoencoders and anomaly detection with machine learning in fraud analytics

sna

• Network analysis of Game of Thrones family ties

r_users_group

• New R Users group in Münster!

timeseries

• Data Science for Business - Time Series Forecasting Part 3: Forecasting with Facebook's Prophet
• Data Science for Business - Time Series Forecasting Part 2: Forecasting with timekit
• Data Science for Business - Time Series Forecasting Part 1: EDA & Data Preparation

forecasting

• Data Science for Business - Time Series Forecasting Part 3: Forecasting with Facebook's Prophet
• Data Science for Business - Time Series Forecasting Part 2: Forecasting with timekit
• Data Science for Business - Time Series Forecasting Part 1: EDA & Data Preparation

data_preparation

• Data Science for Business - Time Series Forecasting Part 1: EDA & Data Preparation

ggplot

• Why I use R for Data Science - An Ode to R
• Social Network Analysis and Topic Modeling of codecentric’s Twitter friends and followers
• Characterizing Twitter followers with tidytext
• Data Science for Business - Time Series Forecasting Part 3: Forecasting with Facebook's Prophet
• Data Science for Business - Time Series Forecasting Part 2: Forecasting with timekit
• Data Science for Business - Time Series Forecasting Part 1: EDA & Data Preparation

tidyverse

• Why I use R for Data Science - An Ode to R
• Social Network Analysis and Topic Modeling of codecentric’s Twitter friends and followers
• Characterizing Twitter followers with tidytext
• Data Science for Business - Time Series Forecasting Part 3: Forecasting with Facebook's Prophet
• Data Science for Business - Time Series Forecasting Part 2: Forecasting with timekit
• Data Science for Business - Time Series Forecasting Part 1: EDA & Data Preparation

timekit

• Data Science for Business - Time Series Forecasting Part 3: Forecasting with Facebook's Prophet
• Data Science for Business - Time Series Forecasting Part 2: Forecasting with timekit

text_analysis

• Social Network Analysis and Topic Modeling of codecentric’s Twitter friends and followers
• How to do Optical Character Recognition (OCR) of non-English documents in R using Tesseract?
• Characterizing Twitter followers with tidytext

tidytext

• Social Network Analysis and Topic Modeling of codecentric’s Twitter friends and followers
• Characterizing Twitter followers with tidytext

tesseract

• How to do Optical Character Recognition (OCR) of non-English documents in R using Tesseract?

ocr

• How to do Optical Character Recognition (OCR) of non-English documents in R using Tesseract?

gitlab

• Migrating from GitHub to GitLab with RStudio (Tutorial)

blogdown

• Why I use R for Data Science - An Ode to R
• Moving my blog to blogdown

hugo

• Moving my blog to blogdown

netlify

• Moving my blog to blogdown

bookdown

• Why I use R for Data Science - An Ode to R

webinar

• From Biology to Industry. A Blogger’s Journey to Data Science.

data_science

• Explore Predictive Maintenance with flexdashboard
• Blockchain & distributed ML - my report from the data2day conference

artificial_intelligence

• Blockchain & distributed ML - my report from the data2day conference

business_intelligence

• Blockchain & distributed ML - my report from the data2day conference

scalability

• Blockchain & distributed ML - my report from the data2day conference

flexdashboard

• Explore Predictive Maintenance with flexdashboard

predictive_maintenance

• Explore Predictive Maintenance with flexdashboard